Merge pull request #5345 from luotao1/ProtoDataProvider

remove usused ProtoDataProvider related codes

paddle/gserver/CMakeLists.txt

@@ -73,7 +73,6 @@ if(MOBILE_INFERENCE)
     list(REMOVE_ITEM GSERVER_SOURCES
          dataproviders/DataProvider.cpp
          dataproviders/MultiDataProvider.cpp
-         dataproviders/ProtoDataProvider.cpp
          dataproviders/PyDataProvider2.cpp
          dataproviders/PyDataProvider.cpp)
 

paddle/gserver/dataproviders/DataProvider.cpp

@@ -16,8 +16,8 @@ limitations under the License. */
 
 #include <unistd.h>
 #include <algorithm>
-#include "ProtoDataProvider.h"
 #include "paddle/utils/Logging.h"
+#include "paddle/utils/Stat.h"
 #include "paddle/utils/StringUtil.h"
 #include "paddle/utils/Util.h"
 
@@ -164,8 +164,6 @@ DataProvider* DataProvider::create(const DataConfig& config,
 
 REGISTER_DATA_PROVIDER(simple, SimpleDataProvider);
 REGISTER_DATA_PROVIDER(dummy, DummyDataProvider);
-REGISTER_DATA_PROVIDER(proto, ProtoDataProvider);
-REGISTER_DATA_PROVIDER(proto_sequence, ProtoSequenceDataProvider);
 
 int64_t DataProvider::getNextBatch(int64_t size, DataBatch* batch) {
   int64_t batchSize = doubleBuffer_ ? getNextBatchFromBuffer(size, batch)

paddle/gserver/dataproviders/ProtoDataProvider.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#pragma once
-
-#include <vector>
-
-#include "DataFormat.pb.h"
-#include "paddle/utils/Stat.h"
-
-#include "DataProvider.h"
-#include "ProtoReader.h"
-
-namespace paddle {
-
-/**
- * @brief Provider data from protobuf data file with each sample
- * specified by proto message
- *
- * DataSample defined in DataFormat.proto.
- *
- * The file format is
- *
- *    header
- *
- *    sample1
- *
- *    sample2
- *
- *    ...
- *
- *    sampleN
- *
- * @note: In the data file, each message is prefixed with its length.
- * The read/write of the protbuf are implemented in ProtoReader.h
- */
-class ProtoDataProvider : public DataProvider {
-public:
-  ProtoDataProvider(const DataConfig& config,
-                    bool useGpu,
-                    bool loadDataAll = true);
-  virtual void reset();
-
-  /**
-   * @note this size includes the sequences which are skipped because they
-   * are longer than the batch size.
-   */
-  virtual int64_t getSize() {
-    int64_t size = sampleNums_;
-    if (usageRatio_ < 1.0f) {
-      size = static_cast<int64_t>(size * usageRatio_);
-    }
-    return size;
-  }
-  virtual void shuffle();
-
-  void loadData(const std::vector<std::string>& fileList);
-
-  virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
-
-protected:
-  /**
-   * @brief load protobuf data from a list of file
-   * @param[in]  fileName  file name of a file which contains
-   * a list of file names
-   */
-  void loadData(const std::string& fileName);
-
-  /**
-   * @brief load protobuf data from file
-   * @param[in]  fileName   data file name
-   */
-  void loadDataFile(const std::string& fileName);
-  /** @brief check data header of each data sample
-   *  @param[in] header     data header read from protobuf data
-   */
-  void checkDataHeader(const DataHeader& header);
-  /**
-   * @brief fill protobuf data into slot_,
-   * slot_ is a vector of ProtoSlot in memory.
-   * @param[in]  sample     data sample read from protobuf data
-   */
-  void fillSlots(const DataSample& sample);
-
-  /**
-   * @brief return true if each sample is one sequence, i.e., independent
-   * of other samples.
-   */
-  inline bool iidData() const { return sequenceStartPositions_.empty(); }
-
-  /**
-   * @brief check that sample is consistent with header_
-   */
-  void checkSample(const DataSample& sample);
-
-  template <class Op>
-  int64_t sequenceLoop(Op op, int64_t size);
-
-  template <class Op>
-  int64_t sampleLoop(Op op, int64_t size);
-
-  template <class Op>
-  int64_t subSampleLoop(Op op, int64_t size, int slot);
-
-  void showDataStats();
-
-protected:
-  struct ProtoVarSlot {
-    std::vector<real> data;
-    std::vector<int> dims;
-  };
-
-  struct ProtoSlot {
-    SlotDef::SlotType type;
-    int dim;
-    std::vector<int> indexData;
-    std::vector<real> denseData;
-    std::vector<sparse_non_value_t> sparseNonValueData;
-    std::vector<sparse_float_value_t> sparseFloatValueData;
-    std::vector<int64_t> indices;
-    std::vector<int64_t> subIndices;
-
-    std::vector<ProtoVarSlot> varDenseData;
-    std::vector<std::vector<int>> varIndices;
-    std::vector<std::string> strData;
-  };
-  DataHeader header_;
-  int numVecSlots_;
-
-  std::vector<ProtoSlot> slots_;
-  size_t sampleNums_;
-
-  /**
-   * The starting position of each sequence in samples.
-   * The last element should be num of samples.
-   * If empty, each sample is one sequence.
-   */
-  std::vector<size_t> sequenceStartPositions_;
-
-  int64_t currentSequenceIndex_;
-
-  // The size should be the number of sequences.
-  std::vector<size_t> shuffledSequenceIds_;
-
-  ThreadLocalD<DataBatch> cpuBatch_;
-  ThreadLocalD<DataBatch> gpuBatch_;
-
-  RWLock lock_;
-  std::vector<StatPtr> nnzStats_;  // stats for number of none-zeros entries
-};
-
-/**
- * @brief Special use for Proto data: instances should contain sparse-non-value
- * slots
- * and label.
- *
- * @note ProtoSequenceDataProvider treats each SPARSE SLOT as a SEQUENCE
- */
-class ProtoSequenceDataProvider : public ProtoDataProvider {
-public:
-  ProtoSequenceDataProvider(const DataConfig& config,
-                            bool useGpu,
-                            bool loadDataAll = true);
-  ~ProtoSequenceDataProvider() {}
-  virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
-};
-
-}  // namespace paddle

paddle/gserver/tests/CMakeLists.txt

@@ -62,17 +62,6 @@ if(NOT WITH_DOUBLE AND NOT MOBILE_INFERENCE)
 endif()
 
 if(NOT MOBILE_INFERENCE)
-################### test_ProtoDataProvider ############
-    add_unittest_without_exec(test_ProtoDataProvider
-        test_ProtoDataProvider.cpp)
-
-    # test_ProtoDataProvider will mkdir as same name,
-    # so if WORKING_DIRECTORY is default directory, then
-    # mkdir will get error.
-    add_test(NAME test_ProtoDataProvider
-        COMMAND ${CMAKE_CURRENT_BINARY_DIR}/test_ProtoDataProvider
-        WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle)
-
 ################## test_Evaluator #######################
     add_unittest(test_Evaluator
         test_Evaluator.cpp)
@@ -110,3 +99,24 @@ add_test(NAME test_PyDataProvider2
    COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/paddle/gserver/tests:${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_PyDataProvider2
         WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle
 )
+
+################# test_CompareSparse ##################
+add_unittest_without_exec(test_CompareSparse
+    test_CompareSparse.cpp)
+if(NOT ON_TRAVIS)
+  add_test(NAME test_CompareSparse
+    COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
+          ${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
+              ./.set_port.sh -p port -n 6
+                  ${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
+    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
+endif()
+
+################ test_CompareTwoNets ######################
+add_unittest_without_exec(test_CompareTwoNets
+    test_CompareTwoNets.cpp)
+add_test(NAME test_CompareTwoNets
+  COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
+        ${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
+        ${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoNets
+    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)

paddle/gserver/tests/proto_files.txt

-./test_ProtoDataProvider/data1.bin
-./test_ProtoDataProvider/data2.bin

paddle/gserver/tests/proto_files_compressed.txt

-./test_ProtoDataProvider/data1.bin.gz
-./test_ProtoDataProvider/data2.bin.gz

paddle/trainer/tests/sample_trainer_config_opt_b.conf → paddle/gserver/tests/sequence_lstm.conf

+#!/usr/bin/env python
 # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,27 +15,50 @@
 
 from paddle.trainer_config_helpers import *
 
-################################### Data Configuration ###################################
-TrainData(ProtoData(files = "trainer/tests/mnist.list"))
-################################### Algorithm Configuration ###################################
-settings(batch_size = 1000,
-         learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
-################################### Network Configuration ###################################
-data = data_layer(name ="input", size=784)
+######################## data source ################################
+dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
+dict_file = dict()
+for line_count, line in enumerate(open(dict_path, "r")):
+    dict_file[line.strip()] = line_count
 
-fc1 = fc_layer(input=data, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/train.list',
+    test_list=None,
+    module='sequenceGen',
+    obj='process',
+    args={"dict_file": dict_file})
 
-fc2 = fc_layer(input=fc1, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
+settings(batch_size=5)
+######################## network configure ################################
+dict_dim = len(open(dict_path, 'r').readlines())
+word_dim = 128
+hidden_dim = 256
+label_dim = 3
+sparse_update = get_config_arg("sparse_update", bool, False)
 
-output = fc_layer(input=[fc1, fc2], size=10,
-                  bias_attr=True,
-                  act=SoftmaxActivation())
+data = data_layer(name="word", size=dict_dim)
 
-lbl = data_layer(name ="label", size=1)
+emb = embedding_layer(
+    input=data,
+    size=word_dim,
+    param_attr=ParamAttr(sparse_update=sparse_update))
 
-cost = classification_cost(input=output, label=lbl)
-outputs(cost)
+with mixed_layer(size=hidden_dim * 4) as lstm_input:
+    lstm_input += full_matrix_projection(input=emb)
+
+lstm = lstmemory(
+    input=lstm_input,
+    act=TanhActivation(),
+    gate_act=SigmoidActivation(),
+    state_act=TanhActivation())
+
+lstm_last = last_seq(input=lstm)
+
+with mixed_layer(
+        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
+    output += full_matrix_projection(input=lstm_last)
+
+outputs(
+    classification_cost(
+        input=output, label=data_layer(
+            name="label", size=1)))

paddle/trainer/tests/sample_trainer_config_opt_a.conf → paddle/gserver/tests/sequence_recurrent.py

+#!/usr/bin/env python
 # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,27 +15,42 @@
 
 from paddle.trainer_config_helpers import *
 
-################################### Data Configuration ###################################
-TrainData(ProtoData(files = "trainer/tests/mnist.list"))
-################################### Algorithm Configuration ###################################
-settings(batch_size = 1000,
-         learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
-################################### Network Configuration ###################################
-data = data_layer(name ="input", size=784)
+######################## data source ################################
+dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
+dict_file = dict()
+for line_count, line in enumerate(open(dict_path, "r")):
+    dict_file[line.strip()] = line_count
 
-fc1 = fc_layer(input=data, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/train.list',
+    test_list=None,
+    module='sequenceGen',
+    obj='process',
+    args={"dict_file": dict_file})
 
-fc2 = fc_layer(input=fc1, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
+settings(batch_size=5)
+######################## network configure ################################
+dict_dim = len(open(dict_path, 'r').readlines())
+word_dim = 128
+hidden_dim = 128
+label_dim = 3
 
-output = fc_layer(input=[fc1, fc2], size=10,
-                  bias_attr=True,
-                  act=SoftmaxActivation())
+# This config is designed to be equivalent with sequence_recurrent_group.py
 
-lbl = data_layer(name ="label", size=1)
+data = data_layer(name="word", size=dict_dim)
 
-cost = classification_cost(input=output, label=lbl)
-outputs(cost)
+emb = embedding_layer(
+    input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
+
+recurrent = recurrent_layer(input=emb, bias_attr=False, act=SoftmaxActivation())
+
+recurrent_last = last_seq(input=recurrent)
+
+with mixed_layer(
+        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
+    output += full_matrix_projection(input=recurrent_last)
+
+outputs(
+    classification_cost(
+        input=output, label=data_layer(
+            name="label", size=1)))

paddle/gserver/tests/sequence_recurrent_group.py

+#!/usr/bin/env python
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer_config_helpers import *
+
+######################## data source ################################
+dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
+dict_file = dict()
+for line_count, line in enumerate(open(dict_path, "r")):
+    dict_file[line.strip()] = line_count
+
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/train.list',
+    test_list=None,
+    module='sequenceGen',
+    obj='process',
+    args={"dict_file": dict_file})
+
+settings(batch_size=5)
+######################## network configure ################################
+dict_dim = len(open(dict_path, 'r').readlines())
+word_dim = 128
+hidden_dim = 128
+label_dim = 3
+
+# This config is designed to be equivalent with sequence_recurrent.py
+
+data = data_layer(name="word", size=dict_dim)
+
+emb = embedding_layer(
+    input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
+
+
+def step(y):
+    mem = memory(name="rnn_state", size=hidden_dim)
+    with mixed_layer(
+            name="rnn_state",
+            size=hidden_dim,
+            bias_attr=False,
+            act=SoftmaxActivation()) as out:
+        out += identity_projection(input=y)
+        out += full_matrix_projection(
+            input=mem, param_attr=ParamAttr(name="___recurrent_layer_0__"))
+    return out
+
+
+recurrent = recurrent_group(name="rnn", step=step, input=emb)
+
+recurrent_last = last_seq(input=recurrent)
+
+with mixed_layer(
+        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
+    output += full_matrix_projection(input=recurrent_last)
+
+outputs(
+    classification_cost(
+        input=output, label=data_layer(
+            name="label", size=1)))

paddle/trainer/tests/test_CompareSparse.cpp → paddle/gserver/tests/test_CompareSparse.cpp

@@ -22,8 +22,7 @@ limitations under the License. */
 using namespace paddle;  // NOLINT
 using namespace std;     // NOLINT
 
-static const string& configFile1 =
-    "trainer/tests/sample_trainer_config_compare_sparse.conf";
+static const string& configFile1 = "gserver/tests/sequence_lstm.conf";
 
 DECLARE_bool(use_gpu);
 DECLARE_string(config);

paddle/trainer/tests/test_CompareTwoNets.cpp → paddle/gserver/tests/test_CompareTwoNets.cpp

@@ -30,8 +30,6 @@ DECLARE_bool(use_gpu);
 DECLARE_string(config);
 DECLARE_string(nics);
 
-DEFINE_string(config_file_a, "", "config of one network to compare");
-DEFINE_string(config_file_b, "", "config of another network to compare");
 DEFINE_bool(need_high_accuracy,
             false,
             "whether need to run in double accuracy");
@@ -42,6 +40,10 @@ DEFINE_double(
 DECLARE_bool(thread_local_rand_use_global_seed);
 DECLARE_int32(seed);
 
+static const string& config_file_a = "gserver/tests/sequence_recurrent.py";
+static const string& config_file_b =
+    "gserver/tests/sequence_recurrent_group.py";
+
 struct ComData {
   vector<Argument> outArgs;
   vector<ParameterPtr> parameters;
@@ -66,6 +68,7 @@ void calcGradient(ComData& data, const string configFile) {
   DataBatch dataBatch;
   int32_t batchSize = trainer.getConfig().opt_config().batch_size();
 
+  trainer.getDataProvider()->reset();
   trainer.getDataProvider()->setSkipShuffle();
   trainer.getDataProvider()->getNextBatch(batchSize, &dataBatch);
 
@@ -167,11 +170,11 @@ void compareGradient(ComData& comDataA, ComData& comDataB) {
 
 TEST(Trainer, create) {
   ComData dataA;
-  calcGradient(dataA, FLAGS_config_file_a);
+  calcGradient(dataA, config_file_a);
   LOG(INFO) << "\n\nforwardBackward of Network A is finished\n\n";
 
   ComData dataB;
-  calcGradient(dataB, FLAGS_config_file_b);
+  calcGradient(dataB, config_file_b);
   LOG(INFO) << "\n\nforwardBackward of the Network B is finished\n\n";
 
   compareGradient(dataA, dataB);

paddle/trainer/tests/CMakeLists.txt

@@ -28,35 +28,7 @@ if(WITH_PYTHON)
           ${PADDLE_SOURCE_DIR}/paddle/.set_port.sh -p port ${CMAKE_CURRENT_BINARY_DIR}/test_TrainerOnePass
       WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
 endif()
-################ test_CompareTwoNets ######################
-add_unittest_without_exec(test_CompareTwoNets
-    test_CompareTwoNets.cpp)
-add_test(NAME test_CompareTwoNets
-  COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
-        ${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoNets
-            --config_file_a=trainer/tests/sample_trainer_config_qb_rnn.conf --config_file_b=trainer/tests/sample_trainer_config_rnn.conf
-    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
-
-############### test_CompareTwoOpts ###################
-add_unittest_without_exec(test_CompareTwoOpts
-    test_CompareTwoOpts.cpp)
-add_test(NAME test_CompareTwoOpts
-  COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
-        ${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoOpts
-            --config_file_a=trainer/tests/sample_trainer_config_opt_a.conf --config_file_b=trainer/tests/sample_trainer_config_opt_b.conf
-            --num_passes=1 --need_high_accuracy=0
-    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
 
-################# test_CompareSparse ##################
-add_unittest_without_exec(test_CompareSparse
-    test_CompareSparse.cpp)
-if(NOT ON_TRAVIS)
-  add_test(NAME test_CompareSparse
-    COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
-              ./.set_port.sh -p port -n 6
-                  ${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
-    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
-endif()
 ################# test_recurrent_machine_generation ###############
 add_unittest_without_exec(test_recurrent_machine_generation
     test_recurrent_machine_generation.cpp)

paddle/trainer/tests/mnist.list

-trainer/tests/mnist_bin_part

paddle/trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.protolist

-./trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.proto_data

paddle/trainer/tests/sample_trainer_config_compare_sparse.conf

-#edit-mode: -*- python -*-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
-
-# Note: when making change to this file, please make sure
-# sample_trainer_config_rnn.conf is changed accordingly so that the uniitest
-# for comparing these two nets can pass (test_CompareTwoNets)
-
-default_initial_std(0.1)
-default_device(0)
-
-word_dim = 999
-l1 = 0
-l2 = 0
-
-model_type("nn")
-
-sparse_update = get_config_arg("sparse_update", bool, False)
-
-TrainData(ProtoData(        
-            type = "proto_sequence",
-            files = ('trainer/tests/train_sparse.list'), 
-            ))
-
-Settings(
-    algorithm='sgd',
-    batch_size=100,
-    learning_rate=0.0001,
-    learning_rate_decay_a=4e-08,
-    learning_rate_decay_b=0.0,
-    learning_rate_schedule='poly',
-)
-
-
-wordvec_dim = 32
-layer2_dim = 16
-layer3_dim = 16
-hidden_dim = 32
-
-slot_names = ["qb", "qw", "tb", "tw"]
-
-def ltr_network(network_name,
-                word_dim=word_dim,
-                wordvec_dim=wordvec_dim,
-                layer2_dim=layer2_dim,
-                layer3_dim=layer3_dim,
-                hidden_dim=hidden_dim,
-                slot_names=slot_names,
-                l1=l1,
-                l2=l2):
-
-    slotnum = len(slot_names)
-    for i in xrange(slotnum):
-        Inputs(slot_names[i] + network_name)
-    for i in xrange(slotnum):
-        Layer(
-            name = slot_names[i] + network_name,
-            type = "data",
-            size = word_dim,
-            device = -1,
-        )
-        Layer(
-            name = slot_names[i] + "_embedding_" + network_name,
-            type = "mixed",
-            size = wordvec_dim,
-            bias = False,
-            device = -1,
-            inputs = TableProjection(slot_names[i] + network_name,
-                                     parameter_name = "embedding.w0",
-                                     decay_rate_l1=l1,
-                                     sparse_remote_update = True,
-                                     sparse_update = sparse_update,
-                                     ),
-        )
-        Layer(
-            name = slot_names[i] + "_rnn1_" + network_name,
-            type = "recurrent",
-            active_type = "tanh",
-            bias = Bias(initial_std = 0,
-                        parameter_name = "rnn1.bias"),
-            inputs = Input(slot_names[i] + "_embedding_" + network_name,
-                           parameter_name = "rnn1.w0")
-        )
-        Layer(
-            name = slot_names[i] + "_rnnlast_" + network_name,
-            type = "seqlastins",
-            inputs = [
-                slot_names[i] + "_rnn1_" + network_name,
-            ],
-        )
-
-    Layer(
-        name = "layer2_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer2_dim,
-        bias = Bias(parameter_name = "layer2.bias"),
-        inputs = [Input(slot_name + "_rnnlast_" + network_name, 
-                        parameter_name = "_layer2_" + slot_name + ".w", 
-                        decay_rate = l2, 
-                        initial_smart = True) for slot_name in slot_names]
-    )
-    Layer(
-        name = "layer3_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer3_dim,
-        bias = Bias(parameter_name = "layer3.bias"),
-        inputs = [
-            Input("layer2_" + network_name, 
-                  parameter_name = "_layer3.w", 
-                  decay_rate = l2, 
-                  initial_smart = True),
-        ]
-    )
-    Layer(
-        name = "output_" + network_name,
-        type = "fc",
-        size = 1,
-        bias = False,
-        inputs = [
-                  Input("layer3_" + network_name,
-                       parameter_name = "_layerO.w"),
-                 ],
-        )
-
-
-ltr_network("left")
-ltr_network("right")
-Inputs("label")
-Layer(
-    name = "label",
-    type = "data",
-    size = 1,
-    )
-Outputs("cost", "qb_rnnlast_left")
-Layer(
-    name = "cost",
-    type = "rank-cost",
-    inputs = ["output_left", "output_right", "label"],
-    )

paddle/trainer/tests/sample_trainer_config_qb_rnn.conf

-#edit-mode: -*- python -*-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
-
-# Note: when making change to this file, please make sure
-# sample_trainer_config_rnn.conf is changed accordingly so that the uniitest
-# for comparing these two nets can pass (test_CompareTwoNets)
-
-default_initial_std(0.1)
-default_device(0)
-
-word_dim = 1451594
-l1 = 0
-l2 = 0
-
-model_type("nn")
-
-sparse_update = get_config_arg("sparse_update", bool, False)
-
-TrainData(ProtoData(        
-            type = "proto_sequence",
-            files = ('trainer/tests/train.list'), 
-            ))
-
-Settings(
-    algorithm='sgd',
-    batch_size=100,
-    learning_rate=0.0001,
-    learning_rate_decay_a=4e-08,
-    learning_rate_decay_b=0.0,
-    learning_rate_schedule='poly',
-)
-
-
-wordvec_dim = 128
-layer2_dim = 96
-layer3_dim = 96
-hidden_dim = 128
-
-slot_names = ["qb", "qw", "tb", "tw"]
-
-def ltr_network(network_name,
-                word_dim=word_dim,
-                wordvec_dim=wordvec_dim,
-                layer2_dim=layer2_dim,
-                layer3_dim=layer3_dim,
-                hidden_dim=hidden_dim,
-                slot_names=slot_names,
-                l1=l1,
-                l2=l2):
-
-    slotnum = len(slot_names)
-    for i in xrange(slotnum):
-        Inputs(slot_names[i] + network_name)
-    for i in xrange(slotnum):
-        Layer(
-            name = slot_names[i] + network_name,
-            type = "data",
-            size = word_dim,
-            device = -1,
-        )
-        Layer(
-            name = slot_names[i] + "_embedding_" + network_name,
-            type = "mixed",
-            size = wordvec_dim,
-            bias = False,
-            device = -1,
-            inputs = TableProjection(slot_names[i] + network_name,
-                                     parameter_name = "embedding.w0",
-                                     decay_rate_l1=l1,
-                                     sparse_remote_update = True,
-                                     sparse_update = sparse_update,
-                                     ),
-        )
-        Layer(
-            name = slot_names[i] + "_rnn1_" + network_name,
-            type = "recurrent",
-            active_type = "tanh",
-            bias = Bias(initial_std = 0,
-                        parameter_name = "rnn1.bias"),
-            inputs = Input(slot_names[i] + "_embedding_" + network_name,
-                           parameter_name = "rnn1.w0")
-        )
-        Layer(
-            name = slot_names[i] + "_rnnlast_" + network_name,
-            type = "seqlastins",
-            inputs = [
-                slot_names[i] + "_rnn1_" + network_name,
-            ],
-        )
-
-    Layer(
-        name = "layer2_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer2_dim,
-        bias = Bias(parameter_name = "layer2.bias"),
-        inputs = [Input(slot_name + "_rnnlast_" + network_name, 
-                        parameter_name = "_layer2_" + slot_name + ".w", 
-                        decay_rate = l2, 
-                        initial_smart = True) for slot_name in slot_names]
-    )
-    Layer(
-        name = "layer3_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer3_dim,
-        bias = Bias(parameter_name = "layer3.bias"),
-        inputs = [
-            Input("layer2_" + network_name, 
-                  parameter_name = "_layer3.w", 
-                  decay_rate = l2, 
-                  initial_smart = True),
-        ]
-    )
-    Layer(
-        name = "output_" + network_name,
-        type = "fc",
-        size = 1,
-        bias = False,
-        inputs = [
-                  Input("layer3_" + network_name,
-                       parameter_name = "_layerO.w"),
-                 ],
-        )
-
-
-ltr_network("left")
-ltr_network("right")
-Inputs("label")
-Layer(
-    name = "label",
-    type = "data",
-    size = 1,
-    )
-Outputs("cost", "qb_rnnlast_left")
-Layer(
-    name = "cost",
-    type = "rank-cost",
-    inputs = ["output_left", "output_right", "label"],
-    )

paddle/trainer/tests/sample_trainer_config_rnn.conf

-#edit-mode: -*- python -*-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
-
-# Note: when making change to this file, please make sure
-# sample_trainer_config_qb_rnn.conf is changed accordingly so that the uniitest
-# for comparing these two nets can pass (test_CompareTwoNets)
-
-default_initial_std(0.1)
-default_device(0)
-
-word_dim = 1451594
-l1 = 0
-l2 = 0
-
-model_type("recurrent_nn")
-
-sparse_update = get_config_arg("sparse_update", bool, False)
-
-TrainData(ProtoData(
-            type = "proto_sequence",
-            files = ('trainer/tests/train.list'), 
-            ))
-
-Settings(
-    algorithm='sgd',
-    batch_size=100,
-    learning_rate=0.0001,
-    learning_rate_decay_a=4e-08,
-    learning_rate_decay_b=0.0,
-    learning_rate_schedule='poly',
-)
-
-
-wordvec_dim = 128
-layer2_dim = 96
-layer3_dim = 96
-hidden_dim = 128
-
-slot_names = ["qb", "qw", "tb", "tw"]
-
-def SimpleRecurrentLayer(name, 
-                         size, 
-                         active_type, 
-                         bias, 
-                         input_layer_name, 
-                         parameter_name,
-                         seq_reversed = False):
-    RecurrentLayerGroupBegin(name + "_layer_group", 
-                             in_links=[input_layer_name], 
-                             out_links=[name],
-                             seq_reversed=seq_reversed)
-    memory_name = Memory(name=name, size=size)
-    Layer(
-        name = name,
-        type = "mixed",
-        size = size,
-        active_type = active_type,
-        bias = bias,
-        inputs = [IdentityProjection(input_layer_name),
-                  FullMatrixProjection(memory_name,
-                                       parameter_name = parameter_name,
-                                       ),
-                  ]
-        )
-    RecurrentLayerGroupEnd(name + "_layer_group")
-
-
-def ltr_network(network_name,
-                word_dim=word_dim,
-                wordvec_dim=wordvec_dim,
-                layer2_dim=layer2_dim,
-                layer3_dim=layer3_dim,
-                hidden_dim=hidden_dim,
-                slot_names=slot_names,
-                l1=l1,
-                l2=l2):
-
-    slotnum = len(slot_names)
-    for i in xrange(slotnum):
-        Inputs(slot_names[i] + network_name)
-    for i in xrange(slotnum):
-        Layer(
-            name = slot_names[i] + network_name,
-            type = "data",
-            size = word_dim,
-            device = -1,
-        )
-        Layer(
-            name = slot_names[i] + "_embedding_" + network_name,
-            type = "mixed",
-            size = wordvec_dim,
-            bias = False,
-            device = -1,
-            inputs = TableProjection(slot_names[i] + network_name,
-                                     parameter_name = "embedding.w0",
-                                     decay_rate_l1=l1,
-                                     sparse_remote_update = True,
-                                     sparse_update = sparse_update,
-                                     ),
-        )
-        SimpleRecurrentLayer(
-            name = slot_names[i] + "_rnn1_" + network_name,
-            size = hidden_dim,
-            active_type = "tanh",
-            bias = Bias(initial_std = 0,
-                        parameter_name = "rnn1.bias"),
-            input_layer_name = slot_names[i] + "_embedding_" + network_name,
-            parameter_name = "rnn1.w0",
-            )
-        Layer(
-            name = slot_names[i] + "_rnnlast_" + network_name,
-            type = "seqlastins",
-            inputs = [
-                slot_names[i] + "_rnn1_" + network_name,
-            ],
-        )
-    Layer(
-        name = "layer2_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer2_dim,
-        bias = Bias(parameter_name = "layer2.bias"),
-        inputs = [Input(slot_name + "_rnnlast_" + network_name, 
-                        parameter_name = "_layer2_" + slot_name + ".w", 
-                        decay_rate = l2, 
-                        initial_smart = True) for slot_name in slot_names]
-    )
-    Layer(
-        name = "layer3_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer3_dim,
-        bias = Bias(parameter_name = "layer3.bias"),
-        inputs = [
-            Input("layer2_" + network_name, 
-                  parameter_name = "_layer3.w", 
-                  decay_rate = l2, 
-                  initial_smart = True),
-        ]
-    )
-    Layer(
-        name = "output_" + network_name,
-        type = "fc",
-        size = 1,
-        bias = False,
-        inputs = [
-                  Input("layer3_" + network_name,
-                       parameter_name = "_layerO.w"),
-                 ],
-        )
-
-
-ltr_network("left")
-ltr_network("right")
-Inputs("label")
-Layer(
-    name = "label",
-    type = "data",
-    size = 1,
-    )
-Outputs("cost", "qb_rnnlast_left")
-Layer(
-    name = "cost",
-    type = "rank-cost",
-    inputs = ["output_left", "output_right", "label"],
-    )

paddle/trainer/tests/testPyDataWrapper.py

@@ -20,28 +20,6 @@ import random
 import json
 import string
 
-
-@provider(slots=[
-    SparseNonValueSlot(10), DenseSlot(2), SparseValueSlot(10), StringSlot(1),
-    IndexSlot(3)
-])
-def processNonSequenceData(obj, filename):
-    with open(filename, "rb") as f:
-        for line in f:
-            slots_str = line.split(';')
-            index = int(slots_str[0])
-            non_values = map(int, slots_str[1].split()[1:])
-            dense = map(float, slots_str[2].split()[1:])
-            strs = slots_str[4].strip().split(' ', 1)[1]
-
-            def __values_mapper__(s):
-                s = s.split(":")
-                return int(s[0]), float(s[1])
-
-            values = map(__values_mapper__, slots_str[3].split()[1:])
-            yield [non_values, dense, values, strs, index]
-
-
 SPARSE_ID_LIMIT = 1000
 SPARSE_ID_COUNT = 100
 SEQUENCE_LIMIT = 50
@@ -146,8 +124,6 @@ def processSubSeqAndGenerateData(obj, name):
 
 
 if __name__ == "__main__":
-    pvd = processNonSequenceData("test.txt")
-    print pvd.getNextBatch(100)
     pvd = processSeqAndGenerateData("_")
     print pvd.getNextBatch(100)
     pvd = processSubSeqAndGenerateData("_")

paddle/trainer/tests/test_CompareTwoOpts.cpp

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#include <gtest/gtest.h>
-#include <paddle/utils/PythonUtil.h>
-#include <algorithm>
-#include <cstdlib>
-
-#include "paddle/trainer/Trainer.h"
-
-using namespace paddle;  // NOLINT
-using namespace std;     // NOLINT
-
-DECLARE_int32(gpu_id);
-
-DECLARE_bool(local);
-DECLARE_bool(use_gpu);
-
-DECLARE_string(config);
-DECLARE_string(nics);
-
-DEFINE_string(config_file_a, "", "config of one network to compare");
-DEFINE_string(config_file_b, "", "config of another network to compare");
-DEFINE_bool(need_high_accuracy,
-            true,
-            "whether need to run in double accuracy (recommended)");
-DEFINE_double(
-    max_diff_ratio,
-    0.0f,
-    "max diff ratio allowed for outputs and parameters (value/gradient)");
-
-struct ComData {
-  vector<Argument> outArgs;
-  vector<ParameterPtr> parameters;
-};
-
-void calcGradient(ComData& data, const string configFile) {
-  FLAGS_config = configFile;
-
-  FLAGS_local = true;
-  FLAGS_use_gpu = false;
-
-  FLAGS_nics = "";
-
-  *ThreadLocalRand::getSeed() = 0;
-  srand(0);
-
-  Trainer trainer;
-  trainer.init(TrainerConfigHelper::createFromFlagConfig(), false);
-
-  data.parameters = trainer.getGradientMachine()->getParameters();
-  trainer.getDataProvider()->setSkipShuffle();
-  trainer.train();
-}
-
-void checkBuffer(real* A,
-                 const char* desA,
-                 real* B,
-                 const char* desB,
-                 size_t len,
-                 size_t width = 1) {
-  int nNum = 0;
-  for (size_t i = 0; i < len; ++i) {
-    real diff = fabs(A[i] - B[i]);
-    if (diff > 0.0f &&
-        diff / std::max(fabs(A[i]), fabs(B[i])) > FLAGS_max_diff_ratio) {
-      nNum++;
-      LOG(INFO) << "Row: " << i / width << ", " << desA << " : " << A[i]
-                << "    " << desB << " : " << B[i];
-    }
-  }
-  EXPECT_EQ(0, nNum);
-  LOG(INFO) << "\n\n";
-}
-
-void compareGradient(ComData& comDataA, ComData& comDataB) {
-  vector<Argument> outArgsA = comDataA.outArgs;
-  vector<Argument> outArgsB = comDataB.outArgs;
-
-  for (size_t i = 0; i < outArgsA.size(); ++i) {
-    CpuMatrix matA(outArgsA[i].value->getHeight(),
-                   outArgsA[i].value->getWidth());
-    CpuMatrix matB(outArgsB[i].value->getHeight(),
-                   outArgsB[i].value->getWidth());
-
-    matA.copyFrom(*outArgsA[i].value);
-    matB.copyFrom(*outArgsB[i].value);
-
-    LOG(INFO) << "\n--------------------------------"
-              << " Check Network Output_" << i << ":"
-              << " -------------------------------------\n";
-    checkBuffer(matA.getData(),
-                "network A output",
-                matB.getData(),
-                "network B output",
-                matA.getElementCnt(),
-                matA.getWidth());
-  }
-
-  vector<ParameterPtr>& parametersA = comDataA.parameters;
-  vector<ParameterPtr>& parametersB = comDataB.parameters;
-
-  LOG(INFO) << "\n\n--------------------------------"
-            << " Check Gradient Machine Parameters:"
-            << " -------------------------------------\n";
-  for (size_t i = 0; i < parametersA.size(); ++i) {
-    ParameterPtr parameterA, parameterB;
-    parameterA = parametersA[i];
-    parameterB = parametersB[i];
-
-    CpuVector paraA(parameterA->getSize());
-    CpuVector paraB(parameterB->getSize());
-    paraA.copyFrom(*parameterA->getBuf(PARAMETER_VALUE));
-    paraB.copyFrom(*parameterB->getBuf(PARAMETER_VALUE));
-
-    LOG(INFO) << "\n\n----------- PARAMETER_VALUE:  " << parameterA->getName()
-              << " ; size : " << paraA.getSize() << " ------------";
-    checkBuffer(paraA.getData(),
-                "Network A",
-                paraB.getData(),
-                "Network B",
-                paraA.getSize());
-
-    CpuVector gradA(*parameterA->getBuf(PARAMETER_GRADIENT));
-    CpuVector gradB(*parameterB->getBuf(PARAMETER_GRADIENT));
-
-    LOG(INFO) << "\n\n----------- PARAMETER_GRADIENT: " << parameterA->getName()
-              << " ; size : " << gradA.getSize() << " -----------";
-    checkBuffer(gradA.getData(),
-                "Network A",
-                gradB.getData(),
-                "Network B",
-                gradA.getSize());
-  }
-}
-
-TEST(Trainer, create) {
-  ComData dataA;
-  calcGradient(dataA, FLAGS_config_file_a);
-  LOG(INFO) << "\n\ntraining of Network A is finished\n\n";
-
-  ComData dataB;
-  calcGradient(dataB, FLAGS_config_file_b);
-  LOG(INFO) << "\n\ntraining of the Network B is finished\n\n";
-
-  compareGradient(dataA, dataB);
-}
-
-int main(int argc, char** argv) {
-  paddle::initMain(argc, argv);
-  testing::InitGoogleTest(&argc, argv);
-  initPython(argc, argv);
-
-#ifndef PADDLE_TYPE_DOUBLE
-  if (FLAGS_need_high_accuracy) {
-    LOG(INFO) << "skip test due to it's need high accuracy";
-    return 0;
-  }
-  if (FLAGS_max_diff_ratio == 0.0f) {
-    FLAGS_max_diff_ratio = 2e-4;
-    LOG(INFO) << "auto set max_diff_ratio " << FLAGS_max_diff_ratio
-              << " in low accuracy mode";
-  }
-#else
-  if (FLAGS_max_diff_ratio == 0.0f) {
-    FLAGS_max_diff_ratio = 2e-7;
-    LOG(INFO) << "auto set max_diff_ratio " << FLAGS_max_diff_ratio
-              << " in high accuracy mode";
-  }
-#endif
-  int ret = RUN_ALL_TESTS();
-  return ret;
-}

paddle/trainer/tests/test_PyDataProviderWrapper.cpp

@@ -25,45 +25,9 @@ limitations under the License. */
 #include <unordered_set>
 #include "picojson.h"
 
-void checkEqual(const paddle::Argument& expect, const paddle::Argument& actual);
 void checkValue(std::vector<paddle::Argument>& arguments, picojson::array& arr);
 const std::string kDir = "./trainer/tests/pydata_provider_wrapper_dir/";
 
-TEST(PyDataProviderWrapper, NoSequenceData) {
-  paddle::DataConfig conf;
-  conf.set_type("py");
-  conf.set_load_data_module(std::string("testPyDataWrapper"));
-  conf.set_load_data_object(std::string("processNonSequenceData"));
-  conf.set_async_load_data(false);
-  conf.clear_files();
-  conf.set_files(kDir + "test_pydata_provider_wrapper.list");
-  paddle::DataProviderPtr provider(paddle::DataProvider::create(conf, false));
-  provider->setSkipShuffle();
-  provider->reset();
-  paddle::DataBatch batchFromPy;
-  provider->getNextBatch(100, &batchFromPy);
-
-  paddle::DataConfig conf2;
-  conf2.set_type("proto");
-  conf2.set_async_load_data(false);
-  conf2.clear_files();
-  conf2.set_files(kDir + "test_pydata_provider_wrapper.protolist");
-
-  provider.reset(paddle::DataProvider::create(conf2, false));
-  provider->setSkipShuffle();
-  provider->reset();
-  paddle::DataBatch batchFromProto;
-  provider->getNextBatch(100, &batchFromProto);
-
-  std::vector<paddle::Argument>& pyArguments = batchFromPy.getStreams();
-  std::vector<paddle::Argument>& protoArguments = batchFromProto.getStreams();
-  EXPECT_EQ(pyArguments.size(), protoArguments.size());
-
-  for (size_t i = 0; i < pyArguments.size(); ++i) {
-    checkEqual(protoArguments[i], pyArguments[i]);
-  }
-}
-
 TEST(PyDataProviderWrapper, SequenceData) {
   paddle::DataConfig conf;
   conf.set_type("py");
@@ -148,66 +112,6 @@ int main(int argc, char** argv) {
   return RUN_ALL_TESTS();
 }
 
-void checkEqual(const paddle::Argument& expect,
-                const paddle::Argument& actual) {
-  if (expect.value) {
-    EXPECT_TRUE(actual.value != nullptr);
-    paddle::Matrix* e = expect.value.get();
-    paddle::Matrix* a = actual.value.get();
-    EXPECT_EQ(e->getWidth(), a->getWidth());
-    EXPECT_EQ(e->getHeight(), a->getHeight());
-    if (dynamic_cast<paddle::CpuSparseMatrix*>(e)) {
-      paddle::CpuSparseMatrix* se = dynamic_cast<paddle::CpuSparseMatrix*>(e);
-      paddle::CpuSparseMatrix* sa = dynamic_cast<paddle::CpuSparseMatrix*>(a);
-      EXPECT_EQ(se->getFormat(), sa->getFormat());
-      EXPECT_EQ(se->getElementCnt(), sa->getElementCnt());
-      size_t rowSize = se->getFormat() == paddle::SPARSE_CSC
-                           ? se->getElementCnt()
-                           : se->getHeight() + 1;
-      size_t colSize = se->getFormat() == paddle::SPARSE_CSC
-                           ? se->getWidth() + 1
-                           : se->getElementCnt();
-      for (size_t i = 0; i < rowSize; ++i) {
-        EXPECT_EQ(se->getRows()[i], sa->getRows()[i]);
-      }
-      for (size_t i = 0; i < colSize; ++i) {
-        EXPECT_EQ(se->getCols()[i], sa->getCols()[i]);
-      }
-      if (se->getValueType() == paddle::FLOAT_VALUE) {
-        EXPECT_EQ(paddle::FLOAT_VALUE, sa->getValueType());
-        for (size_t i = 0; i < se->getElementCnt(); ++i) {
-          EXPECT_EQ(se->getValue()[i], sa->getValue()[i]);
-        }
-      }
-    } else if (dynamic_cast<paddle::CpuMatrix*>(e)) {
-      EXPECT_EQ(e->getElementCnt(), a->getElementCnt());
-      for (size_t i = 0; i < e->getElementCnt(); ++i) {
-        EXPECT_EQ(e->getData()[i], a->getData()[i]);
-      }
-    }
-  }
-
-  if (expect.ids) {
-    EXPECT_TRUE(actual.ids != nullptr);
-    paddle::VectorT<int>* e = expect.ids.get();
-    paddle::VectorT<int>* a = actual.ids.get();
-    EXPECT_EQ(e->getSize(), a->getSize());
-    for (size_t i = 0; i < e->getSize(); ++i) {
-      EXPECT_EQ(e->getData()[i], a->getData()[i]);
-    }
-  }
-
-  if (expect.strs) {
-    EXPECT_TRUE(actual.strs != nullptr);
-    std::vector<std::string>* e = expect.strs.get();
-    std::vector<std::string>* a = actual.strs.get();
-    EXPECT_EQ(e->size(), a->size());
-    for (size_t i = 0; i < e->size(); ++i) {
-      EXPECT_EQ((*e)[i], (*a)[i]);
-    }
-  }
-}
-
 void checkValue(std::vector<paddle::Argument>& arguments,
                 picojson::array& arr) {
   // CHECK SLOT 0, Sparse Value.